NETSE: Small: Towards Better Modeling of Communication Activity Dynamics in Large-Scale Online Social Networks

NETSE：小型：大规模在线社交网络中通信活动动态的更好建模

• 批准号: 1017898
• 负责人: Jennifer Neville
• 金额: $ 49.69万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017898&HistoricalAwards=false
• 关键词: NETSE; Small; Towards; Better; Modeling

Online social networks (OSNs) have witnessed tremendous growth and popularity over the recent years. The huge success and increasing popularity of social networks makes it important to characterize and study their behavior in detail. Recent work in analyzing online social network data has focused primarily on either static social network structure or evolving social networks. However, popular OSNs sites provide mechanisms to form and maintain community over time by facilitating communication, content sharing, and other forms of activities. This research will develop a suite of algorithmic and analytic methods to support the characterization and modeling of activity networks. In particular, we will conduct static and temporal characterization studies of social network activity, study sampling techniques that can preserve graph properties for different communication activity graphs, investigate the fundamental theoretical trade-offs between preserving different properties of the graph, and develop procedural modeling techniques to generate social network activity graphs to better represent the temporal dynamics and burstiness of activity patterns.We firmly believe that the insights garnered from the proposed algorithm development and theoretical analysis will have a significant impact on sampling and analysis in other network-centric domains in addition to OSNs. All the ideas that come out of the research will be incorporated into both graduate as well as undergraduate level networking and data-mining courses.

近年来，在线社交网络（OSN）已经见证了巨大的增长和普及。社交网络的巨大成功和日益普及使得详细描述和研究其行为变得非常重要。最近在分析在线社交网络数据方面的工作主要集中在静态社交网络结构或不断发展的社交网络上。然而，流行的OSN站点提供了通过促进通信、内容共享和其他形式的活动来形成和维护社区的机制。这项研究将开发一套算法和分析方法，以支持活动网络的表征和建模。特别是，我们将进行社会网络活动的静态和时间表征研究，研究可以为不同的通信活动图保留图形属性的采样技术，研究保留图形的不同属性之间的基本理论权衡，并开发过程建模技术来生成社会网络活动图，以更好地表示活动模式的时间动态和突发性。fi我们相信，从所提出的算法开发和理论分析中获得的见解将对OSN之外的其他网络中心域的采样和分析产生重大影响。所有的想法出来的研究将被纳入研究生以及本科层次的网络和数据挖掘课程。